I learned product by
building things first.
I'm Anika, a computer engineer headed to Dartmouth's Master of Engineering Management this fall, with my sights on product in big tech. Somewhere in my degree I kept drifting from "can we build this?" to "should we, and for whom?" That drift is how I ended up in product.
Being an engineer means a spec from me isn't a wish list. It's something I could build myself. So when I make a product call, I know what it costs to build, and what it costs to get wrong.
It started with a Nintendo DS.
I wasn't hooked on the games so much as the hardware: how the dual screens and the touchscreen were built so a kid could just pick it up and know what to do. That was my first proof that a great product takes both engineering sophistication and real design choices, and it's what pointed me toward computer engineering.
The turn toward product came from a failure. On a course project building ML-based route optimization, three days before the demo we tried to connect the modules we'd each built, and they couldn't talk to each other. Every piece worked on its own; no one had designed the system as a whole. That was the lesson: strong parts aren't enough without coordinated decisions around a shared product vision.
It's been the same thread ever since. I don't think knowing how to build something counts for much if the reasoning behind it doesn't hold up. That's why I'm headed to Dartmouth's MEM, and why I want to spend my career in product: where the technical meets the human.
I read a room the way some people read balance sheets.
Moving four times across India growing up taught me to read a room fast, who gets heard, whose ideas get built on, and who goes quiet because they're not sure they belong. That instinct showed up when I led supplemental instruction for an engineering course. I watched 64 students who could follow a worked example but froze on anything unfamiliar, they didn't need another example, they needed to know which method to reach for.
So I built 21 decision-tree worksheets that taught the pattern instead of the answer, each revised from whatever tripped students up the week before. The ones that worked were the ones they could use without me, and the next SI leader reused them.
That's how I think about product: find the real problem, and build the fix so it works without you in the room.
What I bring to a team
I came to product from engineering, and it shows up in how I make decisions.
Anyone can list features. The hard part is cutting the ones that don't earn their place, and that's the call I'd rather get right than get fast.
The decisions I trust start with something real: a user, a metric, a behavior. Assumptions are where I've been most wrong.
I've written the code and owned the backend, so I read feasibility and cost early, and I keep building so that read stays honest as the tech changes.
There's no free call in product, only tradeoffs. I'd rather name the real constraint than ship something that looks finished and isn't.